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Fixed params count and input size for multiple inputs #71

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Fixed params count and input size for multiple inputs #71

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16 changes: 7 additions & 9 deletions torchsummary/torchsummary.py
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Original file line number Diff line number Diff line change
@@ -1,5 +1,6 @@
import torch
import torch.nn as nn
import funcy
from torch.autograd import Variable

from collections import OrderedDict
Expand Down Expand Up @@ -79,27 +80,23 @@ def hook(module, input, output):
line_new = "{:>20} {:>25} {:>15}".format("Layer (type)", "Output Shape", "Param #")
print(line_new)
print("================================================================")
total_params = 0
total_params = sum(p.numel() for p in model.parameters())
trainable_params = sum(p.numel() for p in model.parameters() if p.requires_grad)
total_output = 0
trainable_params = 0
for layer in summary:
# input_shape, output_shape, trainable, nb_params
line_new = "{:>20} {:>25} {:>15}".format(
layer,
str(summary[layer]["output_shape"]),
"{0:,}".format(summary[layer]["nb_params"]),
)
total_params += summary[layer]["nb_params"]
total_output += np.prod(summary[layer]["output_shape"])
if "trainable" in summary[layer]:
if summary[layer]["trainable"] == True:
trainable_params += summary[layer]["nb_params"]
total_output += np.prod(list(funcy.flatten(summary[layer]["output_shape"])))
print(line_new)

# assume 4 bytes/number (float on cuda).
total_input_size = abs(np.prod(input_size) * batch_size * 4. / (1024 ** 2.))
total_input_size = abs(sum([np.prod(input_item) for input_item in input_size]) * batch_size * 4. / (1024 ** 2.))
total_output_size = abs(2. * total_output * 4. / (1024 ** 2.)) # x2 for gradients
total_params_size = abs(total_params.numpy() * 4. / (1024 ** 2.))
total_params_size = abs(total_params * 4. / (1024 ** 2.))
total_size = total_params_size + total_output_size + total_input_size

print("================================================================")
Expand All @@ -113,3 +110,4 @@ def hook(module, input, output):
print("Estimated Total Size (MB): %0.2f" % total_size)
print("----------------------------------------------------------------")
# return summary